1. Technical Field
The present disclosure relates generally to selecting objects in digital visual media. More specifically, one or more embodiments of the present disclosure relate to systems and methods that utilize deep learning techniques to automatically select individuals in digital images.
2. Background and Relevant Art
Recent years have seen a rapid proliferation in the use of mobile digital devices. Individuals and businesses increasingly utilize laptops, tablets, smartphones, handheld devices, and other mobile technology for a variety of daily tasks, and the ubiquitous use of such mobile digital devices has had a significant impact in a variety of fields. For example, with regard to digital photography, individuals and businesses increasingly utilize smartphone cameras to capture digital visual media.
With the rapid adoption of smartphone cameras, the “selfie” has become conspicuously abundant in digital photography. The bulk of these images are captured by casual photographers who often lack the necessary skills to consistently take high-quality images, or to successfully post-process captured digital images. Accordingly, individuals routinely desire to select, segregate, and/or modify a digital representation of an individual in an image separately from other background pixels (e.g., to replace the background or otherwise modify the individual portrayed in the digital image). Accordingly, there is an increasing demand for systems that can identify, segregate, and treat a person captured in a digital image separately from the background.
Some conventional digital image editing systems assist users in segregating an individual portrayed in a digital image from background images. For example, some conventional digital image editing systems permit a user to manually select an individual in a digital image by, for example, manually tracing a boundary line around the individual. Similarly, other convention digital image editing systems can select an individual portrayed in a digital image based on repeated user selections of points or areas that lie inside or outside the represented individual. Unfortunately, these conventional tools have a number of shortcomings.
For example, users often find conventional digital image editing systems tedious and difficult to use. Indeed, the time and effort required to input boundary lines, points, and/or areas often leads to frustration among users of conventional systems. Moreover, users are often disappointed with the results of conventional systems because they fail to accurately segregate individuals portrayed in digital images from background pixels.
These and other problems exist with regard to identifying objects in digital visual media.